Abstract: In one aspect, a method for inspecting features of an image using an image inspection controller that includes a processor communicatively coupled to a memory is described. The method includes receiving, at the processor, an input image, performing, on the input image, one of a semantic segmentation process and an object classification process to generate an output image, and prompting a user to select between approving the displayed output image, and at least one of i) performing an additional semantic segmentation process on the displayed output image, and ii) performing an additional object classification process on the displayed output image.

Abstract: The present disclosure relates to extraction of probe motion estimates from acquired ultrasound image frames. Such image-extracted probe motion data can be used alone or in combination with sensed motion data, such as acquired using an inertial measurement unit (IMU). In certain implementations, the image-extracted probe motion can be used to provide or maintain anatomic context in a sequence of images or to provide guidance to a user.

Abstract: In one aspect, a method for inspecting features of an image using an image inspection controller that includes a processor communicatively coupled to a memory is described. The method includes receiving, at the processor, an input image, performing, on the input image, one of a semantic segmentation process and an object classification process to generate an output image, and prompting a user to select between approving the displayed output image, and at least one of i) performing an additional semantic segmentation process on the displayed output image, and ii) performing an additional object classification process on the displayed output image.

Abstract: Embodiments of the present disclosure relate to techniques for neuromodulation delivery. Based on image data acquired from the subject, control parameters controlling energy application of neuromodulating energy may be dynamically changed during the course of the delivery to maintain desired characteristics of the neuromodulating energy. For example, the beam of the neuromodulating energy may be dynamically adjusted to account for movement of an organ during breathing. In another embodiment, a desired region of interest is identified within the subject based on a trained neural network and the acquired image data.

Abstract: Embodiments of the present disclosure relate to techniques for neuromodulation delivery. Based on image data acquired from the subject, control parameters controlling energy application of neuromodulating energy may be dynamically changed during the course of the delivery to maintain desired characteristics of the neuromodulating energy. For example, the beam of the neuromodulating energy may be dynamically adjusted to account for movement of an organ during breathing. In another embodiment, a desired region of interest is identified within the subject based on a trained neural network and the acquired image data.

Abstract: In accordance with the present disclosure, deep-learning techniques are employed to find anomalies corresponding to bleed events. By way of example, a deep convolutional neural network or combination of such networks may be trained to determine the location of a bleed event, such as an internal bleed event, based on ultrasound data acquired at one or more locations on a patient anatomy. Such a technique may be useful in non-clinical settings.

Abstract: In one aspect, a method for inspecting features of an image using an image inspection controller that includes a processor communicatively coupled to a memory is described. The method includes receiving, at the processor, an input image, performing, on the input image, one of a semantic segmentation process and an object classification process to generate an output image, and prompting a user to select between approving the displayed output image, and at least one of i) performing an additional semantic segmentation process on the displayed output image, and ii) performing an additional object classification process on the displayed output image.

Abstract: Embodiments of the present disclosure relate to techniques for neuromodulation delivery. Based on image data acquired from the subject, control parameters controlling energy application of neuromodulating energy may be dynamically changed during the course of the delivery to maintain desired characteristics of the neuromodulating energy. For example, the beam of the neuromodulating energy may be dynamically adjusted to account for movement of an organ during breathing. In another embodiment, a desired region of interest is identified within the subject based on a trained neural network and the acquired image data.

Abstract: The present disclosure relates to extraction of probe motion estimates from acquired ultrasound image frames. Such image-extracted probe motion data can be used alone or in combination with sensed motion data, such as acquired using an inertial measurement unit (IMU). In certain implementations, the image-extracted probe motion can be used to provide or maintain anatomic context in a sequence of images or to provide guidance to a user.

Abstract: In accordance with the present disclosure, deep-learning techniques are employed to find anomalies corresponding to bleed events. By way of example, a deep convolutional neural network or combination of such networks may be trained to determine the location of a bleed event, such as an internal bleed event, based on ultrasound data acquired at one or more locations on a patient anatomy. Such a technique may be useful in non-clinical settings.